Literature DB >> 30017302

Multiple solid pancreatic lesions: Prevalence and features of non-malignancies on dynamic enhanced CT.

Liang Zhu1, Meng-Hua Dai2, Shi-Tian Wang1, Zheng-Yu Jin3, Qiang Wang4, Timm Denecke5, Bernd Hamm5, Hua-Dan Xue1.   

Abstract

OBJECTIVE: To determine the prevalence of multiple solid pancreatic lesions on dynamic enhanced CT performed for suspected pancreatic diseases, and to identify CT features of non-malignancies.
METHODS: We investigated 8096 consecutive patients who underwent dynamic enhanced CT pancreas protocol at a tertiary center over 40 months. The final clinical /pathological diagnosis served as reference standard. The diagnostic accuracy of dynamic enhanced CT for non-malignancies was calculated. A univariate and multivariate analysis was conducted to identify features that predict non-malignancies.
RESULTS: Multiple solid pancreatic lesions were identified in 121 patients. The prevalence of non-malignancies was 19.8% (24/121). The most common non-malignancy was autoimmune pancreatitis (n = 21; 17.4%). Common lesions with malignant potential included neuroendocrine neoplasia (n = 62; 51.2%), ductal adenocarcinoma (n = 15; 12.4%), metastasis (n = 9; 7.4%), and lymphoma (n = 7; 5.8%). Dynamic enhanced CT had a sensitivity of 79.2% and a specificity of 92.8% for diagnosing non-malignancies. Elevated serum IgG4 level (p < 0.001), hypo-enhancement in arterial phase (p = 0.001), hyper-enhancement in equilibrium phase (p = 0.009) and location in both proximal and distal pancreas (p = 0.036) were predictors of non-malignancies, whereas pancreatic duct morphology and vascular invasion status were not.
CONCLUSION: Multiple solid pancreatic lesions were rare, with a wide spectrum. Dynamic enhanced CT provides clues for identifying non-malignancies.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computed tomography; Metastasis; Neuroendocrine tumours; Pancreatic cancer; Pancreatitis

Mesh:

Year:  2018        PMID: 30017302     DOI: 10.1016/j.ejrad.2018.05.016

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  4 in total

1.  Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network.

Authors:  Shang-Long Liu; Shuo Li; Yu-Ting Guo; Yun-Peng Zhou; Zheng-Dong Zhang; Shuai Li; Yun Lu
Journal:  Chin Med J (Engl)       Date:  2019-12-05       Impact factor: 2.628

Review 2.  Application of artificial intelligence in pancreaticobiliary diseases.

Authors:  Hemant Goyal; Rupinder Mann; Zainab Gandhi; Abhilash Perisetti; Zhongheng Zhang; Neil Sharma; Shreyas Saligram; Sumant Inamdar; Benjamin Tharian
Journal:  Ther Adv Gastrointest Endosc       Date:  2021-02-15

3.  Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades.

Authors:  Tao Zhang; YueHua Zhang; Xinglong Liu; Hanyue Xu; Chaoyue Chen; Xuan Zhou; Yichun Liu; Xuelei Ma
Journal:  Front Oncol       Date:  2021-02-11       Impact factor: 6.244

4.  Multifocal autoimmune pancreatitis: A retrospective study in a single tertiary center of 26 patients with a 20-year literature review.

Authors:  Xin-Ming Huang; Zhen-Shan Shi; Cheng-Le Ma
Journal:  World J Gastroenterol       Date:  2021-07-21       Impact factor: 5.742

  4 in total

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